CQI and rank prediction for list sphere decoding and ML MIMO receivers

ABSTRACT

Systems and methodologies are described that facilitate integrating a list-sphere decoding design in a multiple input-multiple output (MIMO) wireless communication environment. According to various aspects, optimal rank selection and CQI computation for an optimal rank can be performed in conjunction with a non-linear receiver, such as a maximum likelihood (ML) MMSE receiver, a non-linear receiver with a list-sphere decoder, and the like. Optimal rank selection can be performed using a maximum rank selection protocol, a channel capacity-based protocol, or any other suitable protocol that facilitates rank selection, and CQI information can be generated based in part on effective SNRs determined with regard to a selected optimal rank.

CROSS-REFERENCE

This application is a continuation-in-part of U.S. patent applicationentitled “CQI AND RANK PREDICTION FOR LIST SPHERE DECODING AND MIMO MLRECEIVERS,” filed on May 25, 2006, having Attorney Docket No. 050695,which claims the benefit of U.S. Provisional Application Ser. No.60/686,646 entitled “CQI AND RANK PREDICTION IN LIST SPHERE DECODING,”filed on Jun. 1, 2005, and U.S. Provisional Application Ser. No.60/691,722 entitled “METHOD OF LIST SPHERE DECODING FOR MIMO RECEIVERS,”filed on Jun. 16, 2005. The entireties of these applications areincorporated herein by reference.

BACKGROUND

I. Field

The following description relates generally to wireless communications,and more particularly to performing rank calculation in a non-linearreceiver employed in a wireless communication environment.

II. Background

Wireless communication systems have become a prevalent means by which amajority of people worldwide has come to communicate. Wirelesscommunication devices have become smaller and more powerful in order tomeet consumer needs and to improve portability and convenience. Theincrease in processing power in mobile devices such as cellulartelephones has lead to an increase in demands on wireless networktransmission systems. Such systems typically are not as easily updatedas the cellular devices that communicate there over. As mobile devicecapabilities expand, it can be difficult to maintain an older wirelessnetwork system in a manner that facilitates fully exploiting new andimproved wireless device capabilities.

More particularly, frequency division based techniques typicallyseparate the spectrum into distinct channels by splitting it intouniform chunks of bandwidth, for example, division of the frequency bandallocated for wireless communication can be split into 30 channels, eachof which can carry a voice conversation or, with digital service, carrydigital data. Each channel can be assigned to only one user at a time.One known variant is an orthogonal frequency division technique thateffectively partitions the overall system bandwidth into multipleorthogonal subbands. These subbands are also referred to as tones,carriers, subcarriers, bins, and/or frequency channels. Each subband isassociated with a subcarrier that can be modulated with data. With timedivision based techniques, a band is split time-wise into sequentialtime slices or time slots. Each user of a channel is provided with atime slice for transmitting and receiving information in a round-robinmanner. For example, at any given time t, a user is provided access tothe channel for a short burst. Then, access switches to another user whois provided with a short burst of time for transmitting and receivinginformation. The cycle of “taking turns” continues, and eventually eachuser is provided with multiple transmission and reception bursts.

Code division based techniques typically transmit data over a number offrequencies available at any time in a range. In general, data isdigitized and spread over available bandwidth, wherein multiple userscan be overlaid on the channel and respective users can be assigned aunique sequence code. Users can transmit in the same wide-band chunk ofspectrum, wherein each user's signal is spread over the entire bandwidthby its respective unique spreading code. This technique can provide forsharing, wherein one or more users can concurrently transmit andreceive. Such sharing can be achieved through spread spectrum digitalmodulation, wherein a user's stream of bits is encoded and spread acrossa very wide channel in a pseudo-random fashion. The receiver is designedto recognize the associated unique sequence code and undo therandomization in order to collect the bits for a particular user in acoherent manner.

A typical wireless communication network (e.g., employing frequency,time, and code division techniques) includes one or more base stationsthat provide a coverage area and one or more mobile (e.g., wireless)terminals that can transmit and receive data within the coverage area. Atypical base station can simultaneously transmit multiple data streamsfor broadcast, multicast, and/or unicast services, wherein a data streamis a stream of data that can be of independent reception interest to amobile terminal. A mobile terminal within the coverage area of that basestation can be interested in receiving one, more than one or all thedata streams carried by the composite stream. Likewise, a mobileterminal can transmit data to the base station or another mobileterminal. Such communication between base station and mobile terminal orbetween mobile terminals can be degraded due to channel variationsand/or interference power variations.

Conventional wireless systems do not provide support adaptivecommunication techniques in non-linear receivers due to computationalcomplexity, processing overhead, and the like. Thus, there exists a needin the art for a system and/or methodology of improving throughput insuch wireless network systems.

SUMMARY

The following presents a simplified summary of one or more embodimentsin order to provide a basic understanding of such embodiments. Thissummary is not an extensive overview of all contemplated embodiments,and is intended to neither identify key or critical elements of allembodiments nor delineate the scope of any or all embodiments. Its solepurpose is to present some concepts of one or more embodiments in asimplified form as a prelude to the more detailed description that ispresented later.

In accordance with one or more embodiments and corresponding disclosurethereof, various aspects are described in connection with performingrank selection and CQI computation for a non-linear receiver, such as anML-MMSE receiver, in a MIMO wireless communication environment.According to one aspect, a method of calculating rank in a non-linearreceiver in a user device in a wireless communication environment cancomprise receiving a transmission signal at a non-linear receiver,receiving a transmission signal at a non-linear receiver, generatingsubmatrices from a transmission channel over which the transmissionsignal is received, performing a Q-R decomposition on the submatricesand deriving respective upper triangle matrices, determining aneffective SNR for one or more possible transmission ranks, determining achannel capacity metric for all possible ranks of the transmissionsignal, and selecting a rank that maximizes channel capacity fortransmissions. The non-linear receiver can be a maximum likelihood (ML)minimum mean-squared error (MMSE) non-linear receiver, and the wirelesscommunication environment can be a multiple input-multiple output (MIMO)single code word (SCW) wireless communication environment.

Another aspect relates to a wireless communication apparatus thatfacilitates calculating rank in a non-linear receiver in a user devicein a wireless communication environment can comprise a non-linearreceiver that receives a signal with multiple layers, a memory thatstores information related to rank calculation algorithms, and aprocessor coupled to the memory that employs a rank calculationalgorithm to determine an optimum rank for the received signal,generates submatrices from a transmission channel over which thetransmission signal is received, performs a Q-R decomposition on thesubmatrices, derives respective upper triangle matrices, and determinesan effective SNR for one or more possible transmission ranks. Thenon-linear receiver can utilize a list-sphere decoding protocol todecode the received signal. The apparatus can further comprise acapacity mapping component that evaluates transmission capacity for atleast one submatrix of at least one received layer, and a rankevaluation component that identifies an optimal rank having a highestaverage transmission capacity. Additionally, the processor can generatea CQI report for transmission over a reverse link control channel andcan append a 2-bit optimal rank identifier thereto.

Yet another aspect relates to a wireless communication apparatus,comprising: means for performing a non-linear decoding protocol on areceived multiple-layer signal at a user device, means for generatingsubmatrices from a transmission channel over which the transmissionsignal is received; means for performing a Q-R decomposition on thesubmatrices and deriving respective upper triangle matrices; means fordetermining an effective SNR for one or more possible transmissionranks; means for determining a channel capacity metric for each possiblerank of the received signal; and means for transmitting informationrelated to an optimal rank with CQI information over a reverse linkcontrol channel. . The apparatus can additionally comprise means forperforming a list-sphere decoding protocol to decode the receivedsignal, means for capacity mapping the submatrices, and means fordetermining an effective signal-to-noise ratio (SNR) for each submatrix.Moreover, the apparatus can comprise means for generating CQIinformation related to the received signal based at least in part on theeffective SNR associated with the optimal rank. The means fortransmitting can transmit CQI and rank information over the reverse linkcontrol channel approximately every 5 ms.

Still another aspect relates to a computer-readable medium having storedthereon computer-executable instructions for employing a non-lineardecoding protocol in a user device to decode a received multiple-layersignal, generating submatrices from a transmission channel over whichthe transmission signal is received, performing a Q-R decomposition onthe submatrices and deriving respective upper triangle matrices,determining an effective SNR for one or more possible transmissionranks, determining a channel capacity metric for all possible ranks ofthe transmission signal, and selecting a rank that maximizes channelcapacity for transmissions.

A further aspect provides for a processor that executes instructions foremploying a non-linear decoding protocol in a user device to decode areceived multiple-layer signal, generating submatrices from atransmission channel over which the transmission signal is received,performing a Q-R decomposition on the submatrices and derivingrespective upper triangle matrices, determining an effective SNR for oneor more possible transmission ranks, determining a channel capacitymetric for all possible ranks of the transmission signal, and selectinga rank that maximizes channel capacity for transmissions.

To the accomplishment of the foregoing and related ends, the one or moreembodiments comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative aspects ofthe one or more embodiments. These aspects are indicative, however, ofbut a few of the various ways in which the principles of variousembodiments may be employed and the described embodiments are intendedto include all such aspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a wireless network communication system in accordancewith various embodiments presented herein.

FIG. 2 is an illustration of a multiple access wireless communicationsystem according to one or more embodiments.

FIG. 3 is an illustration of a system that facilitates performing rankprediction with an SWC transmitter in a wireless device, in accordancewith one or more aspects.

FIGS. 4-6 illustrate a trellis representation of a list-sphere decodingprotocol and optimization thereof, in accordance with one or moreaspects described herein.

FIG. 7 illustrates a methodology for performing capacity-based rankselection in a non-linear receiver in an access terminal, in accordancewith one or more aspects.

FIG. 8 is an illustration of a methodology for performing maximum rankselection in conjunction with a single code word communication design ina non-linear receiver in an access terminal, in accordance with variousaspects set forth herein.

FIG. 9 is an illustration of a methodology for determining rank in aminimum mean-squared error (MMSE)-based non-linear receiver in an accessterminal, in accordance with one or more aspects set forth herein.

FIG. 10 is an illustration of a user device that facilitates calculatingrank of a received transmission layer in a non-linear receiver employedin a wireless communication environment, in accordance with one or moreaspects set forth herein.

FIG. 11 is an illustration of a system that facilitates updating a rankfor a user device that employs a non-linear receiver in a wirelesscommunication environment in accordance with various aspects.

FIG. 12 is an illustration of a wireless network environment that can beemployed in conjunction with the various systems and methods describedherein.

FIG. 13 is an illustration of an apparatus that facilitates performingrank prediction in a non-linear receiver of an access terminal, inaccordance with one or more aspects.

DETAILED DESCRIPTION

Various embodiments are now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of one or more embodiments. It may be evident, however,that such embodiment(s) may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing one or more embodiments.

As used in this application, the terms “component,” “system,” and thelike are intended to refer to a computer-related entity, eitherhardware, software, software in execution, firmware, middle ware,microcode, and/or any combination thereof. For example, a component maybe, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers. Also, thesecomponents can execute from various computer readable media havingvarious data structures stored thereon. The components may communicateby way of local and/or remote processes such as in accordance with asignal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network such as the Internet with other systemsby way of the signal). Additionally, components of systems describedherein may be rearranged and/or complimented by additional components inorder to facilitate achieving the various aspects, goals, advantages,etc., described with regard thereto, and are not limited to the preciseconfigurations set forth in a given figure, as will be appreciated byone skilled in the art.

Furthermore, various embodiments are described herein in connection witha subscriber station. A subscriber station can also be called a system,a subscriber unit, mobile station, mobile, remote station, access point,remote terminal, access terminal, user terminal, user agent, a userdevice, or user equipment. A subscriber station may be a cellulartelephone, a cordless telephone, a Session Initiation Protocol (SIP)phone, a wireless local loop (WLL) station, a personal digital assistant(PDA), a handheld device having wireless connection capability, or otherprocessing device connected to a wireless modem.

Moreover, various aspects or features described herein may beimplemented as a method, apparatus, or article of manufacture usingstandard programming and/or engineering techniques. The term “article ofmanufacture” as used herein is intended to encompass a computer programaccessible from any computer-readable device, carrier, or media. Forexample, computer-readable media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick,key drive . . . ). Additionally, various storage media described hereincan represent one or more devices and/or other machine-readable mediafor storing information. The term machine-readable medium” can include,without being limited to, wireless channels and various other mediacapable of storing, containing, and/or carrying instruction(s) and/ordata.

Referring now to FIG. 1, a wireless network communication system 100 isillustrated in accordance with various embodiments presented herein.Network 100 can comprise one or more base stations 102 in one or moresectors that receive, transmit, repeat, etc., wireless communicationsignals to each other and/or to one or more mobile devices 104. Eachbase station 102 can comprise a transmitter chain and a receiver chain,each of which can in turn comprise a plurality of components associatedwith signal transmission and reception (e.g., processors, modulators,multiplexers, demodulators, demultiplexers, antennas, etc.), as will beappreciated by one skilled in the art. Mobile devices 104 can be, forexample, cellular phones, smart phones, laptops, handheld communicationdevices, handheld computing devices, satellite radios, globalpositioning systems, PDAs, and/or any other suitable device forcommunicating over wireless network 100.

According to various aspects described herein, when employing aMIMO-MMSE receiver (e.g., in a base station 102 and/or a user device104), the rank prediction and CQI computation (e.g., for a given rank)can be performed with relative ease. However, when utilizing alist-sphere decoder technique, rank prediction and CQI computation canbe more challenging due to the non-linearity of the receiver.Conventional systems and/or methodologies cannot support integration ofa list-sphere decoder design in a MIMO system, and thus cannot exploitthe performance benefits of a list-sphere decoder design. Variousaspects presented herein describe systems and/or methods that canfacilitate implementing a list-sphere decoder in a MIMO system toimprove system performance. For example, MIMO channel capacity can beutilized as a metric for CQI and rank prediction, based at least in parton an assumption of a sphere decoder gap to capacity, as described ingreater detail below.

Referring now to FIG. 2, a multiple access wireless communication system200 according to one or more embodiments is illustrated. System 200 ispresented for illustrative purposes and can be utilized in conjunctionwith various aspects set forth below. A 3-sector base station 202includes multiple antenna groups: one including antennas 204 and 206,another including antennas 208 and 210, and a third including antennas212 and 214. According to the figure, only two antennas are shown foreach antenna group, however, more or fewer antennas may be utilized foreach antenna group. Mobile device 216 is in communication with antennas212 and 214, where antennas 212 and 214 transmit information to mobiledevice 216 over forward link 220 and receive information from mobiledevice 216 over reverse link 218. Mobile device 222 is in communicationwith antennas 204 and 206, where antennas 204 and 206 transmitinformation to mobile device 222 over forward link 226 and receiveinformation from mobile device 222 over reverse link 224.

Each group of antennas and/or the area in which they are designated tocommunicate is often referred to as a sector of base station 202. In oneembodiment, antenna groups each are designed to communicate to mobiledevices in a sector of the areas covered by base station 202. Incommunication over forward links 220 and 226, the transmitting antennasof base station 202 can utilize beam-forming techniques in order toimprove the signal-to-noise ratio of forward links for the differentmobile devices 216 and 222. Additionally, a base station usingbeam-forming to transmit to mobile devices scattered randomly throughits coverage area causes less interference to mobile devices inneighboring cells/sectors than a base station transmitting through asingle antenna to all mobile devices in its coverage area. A basestation may be a fixed station used for communicating with the terminalsand may also be referred to as an access point, a Node B, or some otherterminology. A mobile device may also be called a mobile station, userequipment (UE), a wireless communication device, terminal, accessterminal, user device, or some other terminology.

According to one or more aspects, user devices 216 and 222, as well asbase station 202, can utilize a single code word (SCW) design with rankprediction in conjunction with a MIMO-MMSE receiver. The utilization ofsuch receivers with a SCW design can facilitate closing a performancegap between an SCW design and a multiple code word (MCW)capacity-achieving design. For instance, a list sphere decodingtechnique for an SCW design can achieve up to 1.5 dB gain forsignal-to-noise ratios (SNRs) lower than 15 dB, and can provide up to3.5 dB gain for SNRs greater than 20 dB.

A MIMO receiver design can have two modes of operation: single code word(SCW) and multiple-code word (MCW). The MCW mode can becapacity-achieving because the transmitter can encode data transmittedon each spatial layer independently, potentially with different rates.The receiver employs a successive interference cancellation (SIC)algorithm which works as follows: decode a 1^(st) layer; subtract itscontribution from the received signal after re-encoding; multiply theencoded 1^(st) layer with the “estimated channel”; decode the 2^(nd)layer and so on. This “onion-peeling” approach means that eachsuccessively decoded layer sees increasing SNR and therefore can supporthigher transmission rates. In the absence of error-propagation, the MCWdesign with SIC can achieve capacity. However, such a design requirescareful management of the rates of each spatial layer due to increasedCQI feedback (one CQI for each layer), increased ACK/NACK messaging (onefor each layer), complications in hybrid automatic request (HARQ)protocols since each layer can terminate at different transmissions,performance sensitivity of SIC with Doppler and CQI erasures, increaseddecoding latency requirements since each successive layer cannot bedecoded until prior layers are decoded, increased memory requirements atthe AT with HARQ since all channels and received signals have to bestored for multiple transmissions, etc., in order to performinterference-cancellation, etc.

Accordingly, a SCW mode design, wherein a transmitter encodes datatransmitted on each spatial layer with substantially similar and/oridentical data rates, can be a desirable alternative to an MCW design. Anumber of spatial layers (e.g., rank) is adapted on a packet-by-packetbasis, depending on a channel scenario and SNR, to performrank-prediction. A receiver can employ a low complexity linear receiversuch as MMSE for each of a plurality of received tones. The SCW designcan thus mitigate the above-mentioned implementation complexities of theMCW design. For instance, since 90% of the users in a WAN environmenttypically have SNRs <15 dB, the SCW design can be a desirablealternative to an MCW design.

FIG. 3 is an illustration of a system 300 that facilitates performingrank prediction with an SCW transmitter in a wireless device, inaccordance with one or more aspects. System 300 comprises a turboencoder 302, a QAM mapping component 304, and a rate predictioncomponent 306 that manipulate received inputs and provide an encoded,mapped signal to a demultiplexer 308. Coded symbols are thende-multiplexed by demultiplexer 308 to generate M streams, or layers,such that 1≦M≦min(M_(T),M_(R)), where M is a 2-bit piece of rankinformation specified in a reverse-link CQI control channelapproximately every 5 ms by a receiver 318 via feedback, in addition toa 5-bit CQI feedback signal. The M streams are then spatially mapped byspatial mapping component 310 to M_(T) antennas, after which the rest ofthe transmission processing is similar to the SISO design. A pluralityof respective OFDM modulators 312, 314, and 316, can then modulate theM_(T) streams for transmission by the M_(T) antennas.

Spatial mapping component 310 (e.g., a precoder) can generate an M_(T)×Mmatrix P(k) that maps M symbols on to M_(T) antennas, for each OFDMtone, k. Spatial mapping component 310 can employ a plurality of optionswhen mapping symbols to antennas. According to an example, anM_(R)×M_(T) MIMO channel H(k) can be considered. Precoder matrices canbe chosen so that an equivalent channel matrix H(k)P(k) has improvedfrequency selectivity compared to H(k). Increased frequency selectivitycan be exploited by a decoder to obtain frequency diversity gains.

FIG. 3 further illustrates a number of receive antennas, 1 throughM_(R), each of which is connected to a respective OFDM demodulator 320,322, and 324, which in turn are coupled to a list-sphere decoder (LSD)326. LSD 326 can be implemented on a tone-by-tone basis, where for eachtone, M_(R) received signals are processed to generate the loglikelihood ratio (LLR) for M symbols, where M is the rank. For example,LSD 326 can provide information to a CQI and rank computation component328 to facilitate generating a 5-bit CQI report and a 2-bit rankindicator approximately every 5 ms. LSD 326 can additionally provide 1through M streams of data to a multiplexer 330, which multiplexes thedata streams and provides a single signal to an LLR component 332. LLRcomponent 332 then provides a signal with LLR information to a turbodecoder 334, which decodes the data signal.

An MMSE receiver typically used for SCW design is a linear receiver,which decouples a MIMO channel into a number of SISO channels, where thenumber of SISO channels is equivalent to the rank of the MIMOtransmission. In comparison, LSD 326 can utilize a sphere decodingtechnique that is a low complexity approximation to a maximum likelihood(ML) MIMO decoder (non-linear), and can therefore achieve superiorperformance compared to a linear MMSE receiver. For an orthogonalchannel, the performance of an MMSE receiver and the described LSD 326can be substantially similar and/or identical to one another. Forexample, if M_(R) is the number of receive antennas and M is the rank ofa given MIMO transmission, then the system equation for a given tone canbe defined as:x=Hs+nwhere H is the MIMO channel per tone (M_(R)×M), x is the received signalvector per tone (M_(R)×1), s is the transmit symbol vector per tone(M×1=[s₁ s₂ . . . s_(M)], and where n is the noise vector per tone(M_(R)×1).

The ML MIMO solution is given as:$\hat{s} = {\underset{s \in \Lambda}{\arg\quad\max}{{x - {Hs}}}^{2}}$If implemented directly, the complexity is exponential with the numberof MIMO layers (M), and the symbol constellation order. However, LSD 326can approximate the performance of an ML solution, and thus reducecomputational complexity. According to an example, the QR decompositioncan be defined as: H=QR, where Q is an M_(R)×M matrix and R is an uppertriangular M×M matrix. The zero-forcing solution can be defined as:ŝ=(H*H)⁻¹H*xThen: $\begin{matrix}\begin{matrix}{{\hat{s}}_{ML} = {\underset{s\quad ɛ\quad\Lambda}{\arg\quad\min}{{x - {Hs}}}^{2}}} \\{= {\underset{s\quad ɛ\quad\Lambda}{\arg\quad\min}\left\lbrack {{\left( {s - \hat{s}} \right)^{*}H^{*}{H\left( {s - \hat{s}} \right)}} + {{x^{*}\left( {I - {{H\left( {H^{*}H} \right)}^{- 1}H^{*}}} \right)}x}} \right\rbrack}} \\{{\leq {\underset{s\quad ɛ\quad\Lambda}{\arg\quad\min}\left( {s - \hat{s}} \right)^{*}H^{*}{H\left( {s - \hat{s}} \right)}}} = {r^{2}\quad\left( {r = {{sphere}\quad{radius}}} \right)}} \\{= {\underset{s\quad ɛ\quad\Lambda}{\arg\quad\min}\left( {s - \hat{s}} \right)^{*}R^{*}{R\left( {s - \hat{s}} \right)}}} \\{= {{\underset{s\quad ɛ\quad\Lambda}{\arg\quad\min}{\sum\limits_{i = 1}^{M}{R_{ii}^{2}\left\lbrack {s_{i} - {\hat{s}}_{i} + {\sum\limits_{j = {i + 1}}^{M}{\frac{R_{ij}}{R_{ii}}\left( {s_{j} - {\hat{s}}_{j}} \right)}}} \right\rbrack}^{2}}} = r^{2}}}\end{matrix} & (1)\end{matrix}$Thus, by equation (1), LSD 326 avoids an exhaustive ML search by onlylooking at the points inside a sphere of radius “r”.

LSD component 326 can perform an algorithm as follows. A value i can beset such that i=M. The LHS of equation (1) becomes R_(MM)²∥s_(M)−ŝ_(M)∥², where S_(M)εΛ_(M), which is the constellation used forM^(th) layer. Candidate constellation points can be searched and acandidate constellation point {overscore (s)}_(M) can be selected suchthat R_(MM) ²∥{overscore (s)}_(M)−ŝ_(M)∥²≦r². The value i can then bereset such that i=M−1. The LHS of equation (1) now becomes${{R_{{M - 1},{M - 1}}^{2}\left\lbrack {s_{M - 1} - {\hat{s}}_{M - 1} + {\frac{R_{{M - 1},M}}{R_{{M - 1},{M - 1}}}\left( {{\overset{\_}{s}}_{M} - {\hat{s}}_{M}} \right)}} \right\rbrack}^{2} + {R_{MM}^{2}{{s_{M} - {\hat{s}}_{M}}}^{2}}},{{{where}\quad{\hat{s}}_{M - 1}} \in \Lambda_{M - 1}},$which is the constellation used for the (M−1)^(th) layer. For the SCWdesign, the constellation used for all layers can be the same (e.g.,Λ_(m)=Λ, ∀m=1,2, . . . M). For a given point {overscore (s)}_(M), oneconstellation point, {overscore (s)}_(M−1), can be selected, such that${{R_{{M - 1},{M - 1}}^{2}\left\lbrack {s_{M - 1} - {\hat{s}}_{M - 1} + {\frac{R_{{M - 1},M}}{R_{{M - 1},{M - 1}}}\left( {{\overset{\_}{s}}_{M} - {\hat{s}}_{M}} \right)}} \right\rbrack}^{2} + {R_{MM}^{2}{{s_{M} - {\hat{s}}_{M}}}^{2}}} \leq {{r^{2}.\quad{If}}\quad{no}\quad{point}\quad{\overset{\_}{s}}_{M - 1}}$is available for the choice of {overscore (s)}_(M), then i can be resetto equal M and another {overscore (s)}_(M) can be selected. For a givenpair {overscore (s)}M−1,{overscore (s)}_(M), i can be set such thati=M−2 and a point {overscore (s)}_(M−2) can be selected that fallsinside the radius “r”. Such acts can be reiterated until one solutionvector point [{overscore (s)}_(M),{overscore (s)}_(M−1){overscore(s)}_(M−2), . . . , {overscore (s)}₁] is obtained.

The LHS of equation (1) can then be re-computed assuming the aboveobtained vector point, to obtain a new radius r_(update). Then, r can beredefined such that r←r_(update), and the foregoing can be reiteratedwith the new radius r until the ML solution is obtained. It will beappreciated that the sphere radius shrinks with each iteration, and thusonly a subset of candidate points need to be evaluated before obtainingan ML solution, thereby providing a faster, more efficient solution thancan be obtained using conventional techniques.

LSD 326 can be a MIMO-MAP decoder that generates soft-information forthe turbo-decoder, and is based on sphere decoder principles. As in thesphere-decoder technique described above, a sphere radius “r” can beselected, and similar acts can be performed to select a candidatesolution vector and [{overscore (s)}_(M),{overscore (s)}_(M−1){overscore(s)}_(M−2), . . . , {overscore (s)}₁] and compute the associated costgiven by $\frac{1}{\sigma^{2}}{{{x - {Hs}}}^{2}.}$A function [candidate, cost] can then be added to the “candidate list”.This process can be reiterated until N_(cand) candidate solutions areobtained in the “candidate list”. The remaining candidate solutionvectors within the radius “r” can be added to the candidate list byreplacing the candidate solution vectors with highest costs in thecandidate list.

For example, let M_(c) be the modulation order and let M be the rank ofa MIMO transmission. A total of MM_(c) bits can thus be transmitted ineach tone. The soft extrinsic information (e.g., LLR) for each bitb_(k), ∀k=1,2 . . . MM_(c) is approximated as: $\begin{matrix}{{L_{E}\left( {b_{k}❘x} \right)} \approx {{{\max\limits_{b_{k} = 1}}^{*}\left\{ {{{- \frac{1}{\sigma^{2}}}{{x - {Hs}_{{< b_{k}} = {1 >}}}}^{2}} + {b_{\lbrack k\rbrack}^{T} \cdot L_{A,{\lbrack k\rbrack}}}} \right\}} - {{\max\limits_{b_{k} = 1}}^{*}\left\{ {{{- \frac{1}{\sigma^{2}}}{{x - {Hs}_{{< b_{k}} = {1 >}}}}^{2}} + {b_{\lbrack k\rbrack}^{T} \cdot L_{A,{\lbrack k\rbrack}}}} \right\}}}} & (2)\end{matrix}$where: s_(<b) _(k) _(=1>)=[s₁ s₂ . . . s_(M)]_(<b) _(k) _(=1>) includesall the candidate solution vectors with b_(k)=1; σ² is thenoise-variance; b_([k])=[b₁ . . . b_(k−1) b_(k+1) . . . ] is asub-vector of bits obtained by excluding b_(k); L_(A,[k])=└L_(A,[1]) . .. L_(A,[k−1]) L_(A,[k+1]) . . . ┘ is a vector of apriori LLR informationon all bits corresponding present in the vector b_([k]), and wheremax*(a,b)=ln(e^(a)+e^(b)).

FIGS. 4-6 illustrate a trellis representation of a list-sphere decoderand optimization thereof, in accordance with one or more aspectsdescribed herein. With respect to FIG. 4, a trellis representation 400is illustrated with M stages, corresponding to the rank of the MIMOtransmission, and M_(c) states, corresponding to the number ofconstellation points. According to an example, for a 16 QAMconstellation and Rank 4 transmission, M=4 and M_(c)=4. The optimizationparameter that describes a relationship between complexity andperformance is N_(cand). To further this example, N_(cand)=2 can bedefined. A cost function R_(MM) ²M∥s_(M)−ŝ_(M)∥² can be evaluated forall constellation points, and N_(cand) points that represent a minimumcost can be preserved. If N_(cand)>2^(Mc), then 2^(Mc) points can bepreserved, as indicated by the shaded circles in FIG. 4. For each of theselected points, the cost for M_(c) candidate constellation points forthe (M−1)^(th) rank (layer) can be computed as${R_{{M - 1},{M - 1}}^{2}\left\lbrack {s_{M - 1} - {\hat{s}}_{M - 1} + {\frac{R_{{M - 1},M}}{R_{{M - 1},{M - 1}}}\left( {{\overset{\_}{s}}_{M} - {\hat{s}}_{M}} \right)}} \right\rbrack}^{2} + {R_{MM}^{2}{{{s_{M} - {\hat{s}}_{M}}}^{2}.}}$Several candidate pairs [s_(M),s_(M−1)] can be generated, as indicatedby the dotted lines in the trellis diagram 400. Note that the termR_(MM) ²∥s_(M)−ŝ_(M)∥² is already computed and can be re-used at thispoint. Furthermore,$\frac{R_{{M - 1},M}}{R_{{M - 1},{M - 1}}}\left( {{\overset{\_}{s}}_{M} - {\hat{s}}_{M}} \right)$is computed only N_(cand) times and can be re-used for the remainingcomputations. These observations reduce overall complexity of the listsphere-decoding algorithm.

Now turning to FIG. 5, a trellis diagram 500 is illustrated that depictsfurther path selection in accordance with various aspects and inconjunction with FIG. 4. Of the several dotted paths in FIG. 4, onlyN_(cand) paths have been preserved, as well as the associatedconstellation points from layers M and M−1, which give the minimum costas indicated by solid lines in the trellis diagram 500. Such acts can berepeated for all subsequent layers until a final last stage of thetrellis (corresponding to layer 1) is reached, each time preserving onlyN_(cand) paths and the associated constellation points from all previouslayers. N_(cand) paths and candidate solution vectors can be selected inthis manner, as shown in the final trellis diagram 600 of FIG. 6. Oncethe candidate solutions and the corresponding cost are obtained,equation (2) can be utilized to obtain the LLR for each bit. Theoptimization parameter N_(cand) can be varied depending on theconstellation size. Furthermore, N_(cand) can be varied across stages.

Referring to FIGS. 7-9, methodologies relating to calculating atransmission rank using a non-linear receiver in an access terminal areillustrated. For example, methodologies can relate to calculating atransmission rank using a non-linear receiver with an SCW protocol in anFDMA environment, an OFDMA environment, a CDMA environment, a WCDMAenvironment, a TDMA environment, an SDMA environment, or any othersuitable wireless environment. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofacts, it is to be understood and appreciated that the methodologies arenot limited by the order of acts, as some acts may, in accordance withone or more embodiments, occur in different orders and/or concurrentlywith other acts from that shown and described herein. For example, thoseskilled in the art will understand and appreciate that a methodologycould alternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all illustrated actsmay be required to implement a methodology in accordance with one ormore embodiments.

FIG. 7 illustrates a methodology 700 for performing Q-Rdecomposition-based rank selection in a non-linear receiver in a userdevice, in accordance with one or more aspects. Method 700 is describedusing a 4-layer transmission, although more or fewer layers may beutilized in conjunction with the systems and methods described herein aswill be appreciated by those skilled in the art. From the estimated 4×4MIMO channel, 4×1, 4×2, 4×3 and 4×4 channel sub-matrices can be derived,denoted as Ĥ(1)=H_(4×1), Ĥ(2)=H_(4×2), Ĥ(3)=H_(4×3),Ĥ(4)=H_(4×4), at702, and can be evaluated. Matrix evaluation can comprise performingrespective Q-R matrix decompositions at 704 to derive upper triangularmatrices R(1), R(2), . . . R(4) as follows:R(1)=QR[H _(4×1) ],R(2)=QR[H _(4×2) ],R(3)=QR[H _(4×3) ],R(4)=QR[H_(4×4)]Method 700 may be performed by a QRM/list-sphere decoder, such as aMAP/ML decoder, which performs joint processing of all layers andestimates symbols from all layers. Assuming that the symbol estimatesfrom all layers are accurately estimated, an effective SNR calculationmay be performed at 706, and expressed as: $\begin{matrix}{{{effSNR}\left\lbrack {{M_{\quad{{poss},}}\sigma^{2}},k} \right\rbrack} = \frac{R_{kk}\left( {M\quad}_{poss} \right)}{M_{\quad{poss}}\sigma^{2}}} & {{k = 1},2,\ldots\quad,}\end{matrix}M_{\quad{poss}}$where M_(poss)=1,2, . . . ,4 and indicates the possible transmissionranks, where sigmaˆ2 is the noise variance, and where R_(kk) (M_(poss))is the (k,k)-th element of the matrix R(M_(poss)). The effective SNRcomputed from the above equation may be averaged over several tones andsymbols in a current Frame, using, for example, a 64-QAM constrainedcapacity mapping, to give an averaged effective SNR number, denoted as${\overset{\_}{effSNR}\left\lbrack M_{\quad{poss}} \right\rbrack}.$At 708, the rank that maximizes capacity may be chosen as the optimumrank, e.g.,$M_{opt} = {\underset{{j = 1},2,{\ldots\quad{\min{({{MR},{MT}})}}}}{\arg\quad\max}j \times {{{cap}_{64}\left\lbrack \overset{\_}{{effSNR}\lbrack j\rbrack} \right\rbrack}.}}$The spectral efficiency/layer for the MIMO-SCW design may be given as:{overscore (C)}=cap₆₄[{overscore (effSNR[M_(opt)]])}.The CQI (assuming the above rank) for the MIMO-SCW transmission may bequantized, at 710, to the required number of bits as: CQI=Q[{overscore(effSNR)}[M_(opt)]]. The CQI and rank may then be fed-back using theRL-CTRL channel, at 712.

FIG. 8 is an illustration of a methodology 800 for performing maximumrank selection in conjunction with a single code word communicationdesign in a non-linear receiver in a wireless terminal, in accordancewith various aspects set forth herein. According the method 800, maximumrank is selected for a MIMO transmission, and code-rate and QAM may bevaried to achieve a desired spectral efficiency. At 802, the spectralefficiency for the SCW design is calculated from the MIMO channel as:$C = {\frac{1}{M}\log\quad{\det\left\lbrack {I + {\frac{E_{s}}{M\quad{\Gamma\sigma}^{2}}{HH}^{*}}} \right\rbrack}{bps}\text{/}{Hz}}$where M is MIMO-SCW transmission rank, E_(s) is total transmit poweracross all antennas, Γ represents the gap to capacity (e.g., turbodecoder gap, sphere decoder gap, channel estimation loss, . . . ), andσ² is the noise variance per receive antenna. At 804, the spectralefficiency can be averaged across all tones and multiple OFDM symbols ina frame to generate an average spectral efficiency {overscore (C)}. At806, CQI (e.g., assuming rank 4 in a 4-layer scenario, as described withregard to FIG. 7) for the MIMO-SCW transmission can be calculated bycomputing the AWGN effective SNR, such that$\overset{\_}{C} = {\log\left\lbrack {1 + \frac{effSNR}{\Gamma}} \right\rbrack}$and quantizing it to the required bits such that CQI=Q[effSNR]. It willbe appreciated that higher code-rates can be enabled for 4-QAM and16-QAM systems as opposed to, for instance, a 64-QAM system.

FIG. 9 is an illustration of a methodology 900 for determining rank inan access terminal having a minimum mean-squared error (MMSE)-basednon-linear receiver, in accordance with one or more aspects set forthherein. At 902, submatrices for each of a plurality of transmissionlayers can be generated, as described above. At 904, successiveinterference cancellation (SIC) capacity for each matrix can bedetermined. Each layer can be capacity-mapped and an effective SNR canbe determined there for at 906. According to an example, it may bedetermined that rank 2 is optimal in a particular scenario. In such acase, diagonal elements in a matrix for a layer corresponding to rank 2can be evaluated and averaged. Additionally, an element of a matrixcorresponding to a layer of rank 1 can be evaluated, and the capacitythere for can be summed with the average of the capacities for theelements of the rank-2 layer (e.g., because such layers are successive).Thus, such capacities can be added at 908, such that total capacity forrank 2 can be equal to the average capacity for all layers of rank 2plus the capacity of the rank-1 layer. At 910, the method can berepeated for all ranks. At 912, a rank exhibiting the highest overallcapacity can be selected. The selected rank can then be returned alongwith a CQI report approximately every 5 ms as described above.

It will be appreciated that, in accordance with one or more aspectsdescribed herein, inferences can be made regarding rank evaluation in anon-linear receiver, when to employ an algorithm associated therewith,whether to employ a non-linear receiver protocol, etc. As used herein,the term to “infer” or “inference” refers generally to the process ofreasoning about or inferring states of the system, environment, and/oruser from a set of observations as captured via events and/or data.Inference can be employed to identify a specific context or action, orcan generate a probability distribution over states, for example. Theinference can be probabilistic—that is, the computation of a probabilitydistribution over states of interest based on a consideration of dataand events. Inference can also refer to techniques employed forcomposing higher-level events from a set of events and/or data. Suchinference results in the construction of new events or actions from aset of observed events and/or stored event data, whether or not theevents are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources.

According to an example, one or methods presented above can includemaking inferences regarding whether to employ a non-linear decoder asdescribed above in a system comprising dual-decoding capabilities, etc.For instance, in a user device that has both a linear decoder and anon-linear decoder, inferences can be made regarding resourceavailability, computational overhead associated with employing eitherdecoder, signal strength for a received transmission, and/or any othersuitable information related to making a determination of whether alinear decoder protocol will suffice to achieve a desired outcome orwhether a non-linear decoding scheme is preferable. In such cases,inferences can be made to facilitate preserving system resources,improving a user's communication experience, etc. For example, ifbattery life is in issue, it may be desirable to forego rank-relatedprotocols in conjunction with a non-linear receiver in order to reducecomputational overhead and preserve battery life as long as possible.Conversely, in a case where battery life is not in issue, it can beinferred that utilization of the non-linear receiver is desirable toenhance a communication experience even in the event that greaterprocessing power may be required. It will be appreciated that theforegoing example is illustrative in nature and is not intended to limitthe number of inferences that can be made or the manner in which suchinferences are made in conjunction with the various embodiments and/ormethods described herein.

FIG. 10 is an illustration of a user device 1000 that facilitatescalculating rank of a received transmission layer in a non-linearreceiver employed in a wireless communication environment, in accordancewith one or more aspects set forth herein. User device 1000 comprises areceiver 1002 that receives a signal from, for instance, a receiveantenna (not shown), and performs typical actions thereon (e.g.,filters, amplifies, downconverts, etc.) the received signal anddigitizes the conditioned signal to obtain samples. Receiver 1002 can bea non-linear receiver, such as a maximum likelihood (ML)-MMSE receiveror the like. A demodulator 1004 can demodulate and provide receivedpilot symbols to a processor 1006 for channel estimation. Processor 1006can be a processor dedicated to analyzing information received byreceiver 1002 and/or generating information for transmission by atransmitter 1016, a processor that controls one or more components ofuser device 1000, and/or a processor that both analyzes informationreceived by receiver 1002, generates information for transmission bytransmitter 1016, and controls one or more components of user device1000.

User device 1000 can additionally comprise memory 1008 that isoperatively coupled to processor 1006 and that stores informationrelated to calculated ranks for user device 1000, a rank calculationprotocol, lookup table(s) comprising information related thereto, andany other suitable information for supporting list-sphere decoding tocalculate rank in a non-linear receiver in a wireless communicationsystem as described herein. Memory 1008 can additionally store protocolsassociated rank calculation, matrix generation, etc., such that userdevice 1000 can employ stored protocols and/or algorithms to achieverank determination in a non-linear receiver as described herein.

It will be appreciated that the data store (e.g., memories) componentsdescribed herein can be either volatile memory or nonvolatile memory, orcan include both volatile and nonvolatile memory. By way ofillustration, and not limitation, nonvolatile memory can include readonly memory (ROM), programmable ROM (PROM), electrically programmableROM (EPROM), electrically erasable ROM (EEPROM), or flash memory.Volatile memory can include random access memory (RAM), which acts asexternal cache memory. By way of illustration and not limitation, RAM isavailable in many forms such as synchronous RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM(DRRAM). The memory 1008 of the subject systems and methods is intendedto comprise, without being limited to, these and any other suitabletypes of memory.

Processor 1006 is further coupled to a capacity-mapping component 1010that can facilitate mapping a capacity for one or more received layersin order to facilitate rank determination there for. User device canfurthermore comprise rank evaluator 1012 that assesses a rank for eachreceived layer as described here with regard to the preceding systemsand methods. For instance, rank evaluator 1012 can utilize acapacity-based rank selection algorithm, a maximum rank selectionalgorithm, or any other suitable rank selection protocol to evaluaterank for a received layer in a non-linear receiver. User device 1000still further comprises a symbol modulator 1014 and a transmitter 1016that transmits the modulated signal.

FIG. 11 is an illustration of a system 1100 that facilitates updating arank for a user device that employs a non-linear receiver in a wirelesscommunication environment in accordance with various aspects. System1100 comprises a base station 1102 with a receiver 1110 that receivessignal(s) from one or more user devices 1104 through a plurality ofreceive antennas 1106, and a transmitter 1124 that transmits to the oneor more user devices 1104 through a transmit antenna 1108. Receiver 1110can receive information from receive antennas 1106 and is operativelyassociated with a demodulator 1112 that demodulates receivedinformation. Demodulated symbols are analyzed by a processor 1114 thatis similar to the processor described above with regard to FIG. 10, andwhich is coupled to a memory 1116 that stores information related touser ranks, lookup tables related thereto, and/or any other suitableinformation related to performing the various actions and functions setforth herein. Processor 1114 is further coupled to a rank adjuster 1118that facilitates updating rank information associated with one or morerespective user devices 1104 based on information received with a CQIreport. Such information can be received from one or more user devices1104 approximately every 5 ms, as is standard interval for CQI reporttransmissions.

A modulator 1122 can multiplex a signal for transmission by atransmitter 1124 through transmit antenna 1108 to user devices 1104.Rank adjuster 1118 can append information to a signal related to anupdated optimum rank for a given transmission stream for communicationwith a user device 1104, which can be transmitted to user device 1104 toprovide an indication that a new optimum channel has been identified andacknowledged. In this manner, base station 1102 can interact with a userdevice 1104 that provides rank update information and employs alist-sphere decoding protocol in conjunction with a non-linear receiver,such as an ML-MIMO receiver, etc., as described above with regard to thepreceding figures.

FIG. 12 shows an exemplary wireless communication system 1200. Thewireless communication system 1200 depicts one base station and oneterminal for sake of brevity. However, it is to be appreciated that thesystem can include more than one base station and/or more than oneterminal, wherein additional base stations and/or terminals can besubstantially similar or different for the exemplary base station andterminal described below. In addition, it is to be appreciated that thebase station and/or the terminal can employ the systems (FIGS. 1-6 and10-11) and/or methods (FIGS. 7-9) described herein to facilitatewireless communication there between.

Referring now to FIG. 12, on a downlink, at access point 1205, atransmit (TX) data processor 1210 receives, formats, codes, interleaves,and modulates (or symbol maps) traffic data and provides modulationsymbols (“data symbols”). A symbol modulator 1215 receives and processesthe data symbols and pilot symbols and provides a stream of symbols. Asymbol modulator 1220 multiplexes data and pilot symbols and providesthem to a transmitter unit (TMTR) 1220. Each transmit symbol may be adata symbol, a pilot symbol, or a signal value of zero. The pilotsymbols may be sent continuously in each symbol period. The pilotsymbols can be frequency division multiplexed (FDM), orthogonalfrequency division multiplexed (OFDM), time division multiplexed (TDM),frequency division multiplexed (FDM), or code division multiplexed(CDM).

TMTR 1220 receives and converts the stream of symbols into one or moreanalog signals and further conditions (e.g., amplifies, filters, andfrequency upconverts) the analog signals to generate a downlink signalsuitable for transmission over the wireless channel. The downlink signalis then transmitted through an antenna 1225 to the terminals. Atterminal 1230, an antenna 1235 receives the downlink signal and providesa received signal to a receiver unit (RCVR) 1240. Receiver unit 1240conditions (e.g., filters, amplifies, and frequency downconverts) thereceived signal and digitizes the conditioned signal to obtain samples.A symbol demodulator 1245 demodulates and provides received pilotsymbols to a processor 1250 for channel estimation. Symbol demodulator1245 further receives a frequency response estimate for the downlinkfrom processor 1250, performs data demodulation on the received datasymbols to obtain data symbol estimates (which are estimates of thetransmitted data symbols), and provides the data symbol estimates to anRX data processor 1255, which demodulates (i.e., symbol demaps),deinterleaves, and decodes the data symbol estimates to recover thetransmitted traffic data. The processing by symbol demodulator 1245 andRX data processor 1255 is complementary to the processing by symbolmodulator 1215 and TX data processor 1210, respectively, at access point1205.

On the uplink, a TX data processor 1260 processes traffic data andprovides data symbols. A symbol modulator 1265 receives and multiplexesthe data symbols with pilot symbols, performs modulation, and provides astream of symbols. A transmitter unit 1270 then receives and processesthe stream of symbols to generate an uplink signal, which is transmittedby the antenna 1235 to the access point 1205.

At access point 1205, the uplink signal from terminal 1230 is receivedby the antenna 1225 and processed by a receiver unit 1275 to obtainsamples. A symbol demodulator 1280 then processes the samples andprovides received pilot symbols and data symbol estimates for theuplink. An RX data processor 1285 processes the data symbol estimates torecover the traffic data transmitted by terminal 1230. A processor 1290performs channel estimation for each active terminal transmitting on theuplink. Multiple terminals may transmit pilot concurrently on the uplinkon their respective assigned sets of pilot subbands, where the pilotsubband sets may be interlaced.

Processors 1290 and 1250 direct (e.g., control, coordinate, manage,etc.) operation at access point 1205 and terminal 1230, respectively.Respective processors 1290 and 1250 can be associated with memory units(not shown) that store program codes and data. Processors 1290 and 1250can also perform computations to derive frequency and impulse responseestimates for the uplink and downlink, respectively.

For a multiple-access system (e.g., FDMA, OFDMA, CDMA, TDMA, etc.),multiple terminals can transmit concurrently on the uplink. For such asystem, the pilot subbands may be shared among different terminals. Thechannel estimation techniques may be used in cases where the pilotsubbands for each terminal span the entire operating band (possiblyexcept for the band edges). Such a pilot subband structure would bedesirable to obtain frequency diversity for each terminal. Thetechniques described herein may be implemented by various means. Forexample, these techniques may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsused for channel estimation may be implemented within one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof. With software, implementation can bethrough modules (e.g., procedures, functions, and so on) that performthe functions described herein. The software codes may be stored inmemory unit and executed by the processors 1290 and 1250.

FIG. 13 is an illustration of an apparatus 1300 that facilitatesperforming rank prediction in a non-linear receiver of an accessterminal, in accordance with one or more aspects. Apparatus 1300 isrepresented as a series of interrelated functional blocks, or “modules,”which can represent functions implemented by a processor, software, orcombination thereof (e.g., firmware). For example, apparatus 1300 mayprovide modules for performing various acts such as are described abovewith regard to the preceding figures. Apparatus 1300 comprises a logicalmodule for generating anD evaluating Channel submatrices 1302, which isoperatively coupled to a logical module for performing QR matrixdecomposition 1304. Apparatus 1300 further comprises a logical modulefor calculating effective SNR(s) 1306, and a logical module forselecting an optimum rank 1308. Apparatus 1300 still further comprises alogical module for quantizing CQI information 1310 (e.g., quantizing aneffective SNR for a selected optimum rank to generate CQI informationthere for), and a logical module for feeding back CQI and rankinformation 1312 from the access terminal to an access point, over areverse link control channel. It is to be understood that apparatus 1300and the various modules comprised thereby may carryout the methodsdescribed above and/or may impart any necessary functionality to thevarious systems described herein.

For a software implementation, the techniques described herein may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. The software codes may be storedin memory units and executed by processors. The memory unit may beimplemented within the processor or external to the processor, in whichcase it can be communicatively coupled to the processor via variousmeans as is known in the art.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the aforementioned embodiments, but one of ordinary skill inthe art may recognize that many further combinations and permutations ofvarious embodiments are possible. Accordingly, the described embodimentsare intended to embrace all such alterations, modifications andvariations that fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

1. A method of calculating rank in a non-linear transceiver in awireless communication transceiver, comprising: receiving a transmissionsignal at a non-linear receiver; generating submatrices from atransmission channel over which the transmission signal is received;performing a Q-R decomposition on the submatrices and derivingrespective upper triangle matrices; determining an effective SNR for oneor more possible transmission ranks; determining a channel capacitymetric for all possible ranks of the transmission signal; and selectinga rank that maximizes channel capacity for transmissions.
 2. The methodof claim 1, further comprising employing a list-sphere decodingalgorithm to decode the received signal transmission.
 3. The method ofclaim 2, further comprising identifying a rank with a highest averagecapacity.
 4. The method of claim 3, further comprising calculating a CQIand feeding back the CQI and rank information using a reverse linkcontrol channel.
 5. The method of claim 1, wherein the non-linearreceiver is a maximum likelihood based non-linear receiver.
 6. Themethod of claim 1, wherein the wireless communication transceiver is amultiple input-multiple output (MIMO) single code word (SCW) wirelesstransceiver.
 7. An apparatus that facilitates calculating rank in anon-linear transceiver in a user device in a wireless communicationenvironment, comprising: a non-linear receiver that receives a signalwith multiple layers; a memory that stores information related to rankcalculation algorithms; and a processor coupled to the memory thatemploys a rank calculation algorithm to determine an optimum rank forthe received signal, generates submatrices from a transmission channelover which the transmission signal is received, performs a Q-Rdecomposition on the submatrices, derives respective upper trianglematrices, and determines an effective SNR for one or more possibletransmission ranks.
 8. The apparatus of claim 7, the non-linear receiveremploys a list-sphere decoding protocol to decode the received signal.9. The apparatus of claim 8, wherein the processor generates a CQIreport for transmission over a reverse link control channel and appendsa 2-bit optimal rank identifier thereto.
 10. A wireless communicationapparatus, comprising: means for performing a non-linear decodingprotocol on a received multiple-layer signal in a user device; means forgenerating submatrices from a transmission channel over which thetransmission signal is received; means for performing a Q-Rdecomposition on the submatrices and deriving respective upper trianglematrices; means for determining an effective SNR for one or morepossible transmission ranks; means for determining a channel capacitymetric for each possible rank of the received signal; and means fortransmitting information related to an optimal rank with CQI informationover a reverse link control channel.
 11. The apparatus of claim 10,further comprising means for performing a list-sphere decoding protocolto decode the received signal.
 12. The apparatus of claim 11, furthercomprising means for generating CQI information related to the receivedsignal based at least in part on the effective SNR of a layer having theoptimal rank.
 13. The apparatus of claim 10, the means for transmittingtransmits a CQI and rank information over the reverse link controlchannel approximately every 5 ms.
 14. A computer-readable medium havingstored thereon computer-executable instructions for: employing anon-linear decoding protocol to decode a received multiple-layer signalin a user device; generating submatrices from a transmission channelover which the transmission signal is received; performing a Q-Rdecomposition on the submatrices and deriving respective upper trianglematrices; determining an effective SNR for one or more possibletransmission ranks; determining a channel capacity metric for allpossible ranks of the transmission signal; and selecting a rank thatmaximizes channel capacity for transmissions.
 15. The computer-readablemedium of claim 14, further comprising instructions for employing alist-sphere decoding algorithm to decode the received signaltransmission.
 16. The computer-readable medium of claim 15, furthercomprising instructions for determining a transmission capacity for eachof the submatrices and averaging capacities for each rank.
 17. Thecomputer-readable medium of claim 16, further comprising instructionsfor identifying a rank with a highest average capacity.
 18. Thecomputer-readable medium of claim 17, further comprising instructionsfor calculating a CQI and feeding back the CQI and rank informationusing a reverse link control channel.
 19. A processor that executesinstructions for increasing throughput in a wireless communicationenvironment, the instructions comprising: employing a non-lineardecoding protocol to decode a received multiple-layer signal in a userdevice; generating submatrices from a transmission channel over whichthe transmission signal is received; performing a Q-R decomposition onthe submatrices and deriving respective upper triangle matrices;determining an effective SNR for one or more possible transmissionranks; determining a channel capacity metric for all possible ranks ofthe transmission signal; and selecting a rank that maximizes channelcapacity for transmissions.
 20. The processor of claim 19, theinstructions further comprising employing a list-sphere decodingalgorithm to decode the received signal transmission.
 21. The processorof claim 20, the instructions further comprising identifying a rank witha highest average capacity.
 22. The processor of claim 21, theinstructions further comprising calculating a CQI and feeding back theCQI and rank information using a reverse link control channel.
 23. Theprocessor of claim 22, wherein the non-linear decoding protocol is amaximum likelihood (ML) non-linear decoding protocol.